Method and apparatus for receiving a financing offer from an image

ABSTRACT

Some aspects of the invention relate to a mobile apparatus including an image sensor configured to convert an optical image into an electrical signal. The optical image includes an image of a vehicle license plate. The mobile apparatus includes a license plate detector configured to process the electrical signal to recover information from the vehicle license plate image. The mobile apparatus includes an interface configured to transmit the vehicle license plate information to a remote apparatus and receive a vehicle financing offer in response to the transmission.

CLAIM OF BENEFIT TO PRIOR APPLICATIONS

This application claims the benefit of priority from U.S. patentapplication Ser. No. 14/318,397, entitled “METHOD AND SYSTEM FORPROVIDING A VALUATION DERIVED FROM AN IMAGE,” filed on Jun. 27, 2014,U.S. patent application Ser. No. 14/455,841, entitled “METHOD AND SYSTEMFOR PROVIDING A LISTING OF VEHICLES BASED ON VEHICLE LICENSE PLATEINFORMATION FROM A SIMILAR VEHICLE,” filed on Aug. 8, 2014, and U.S.patent application Ser. No. 14/613,323 entitled “METHOD AND APPARATUSFOR RECOVERING LICENSE PLATE INFORMATION FROM AN IMAGE,” filed on Feb.3, 2015.

BACKGROUND Field

The present disclosure relates generally to a method and apparatus fordetecting license plate information from an image of a license plate andmore specifically, detecting license plate information from an opticalimage, captured by a mobile apparatus, that includes a license plateimage and several other object images.

Background

In recent years, collecting still images of license plates has become acommon tool used by authorities to catch the drivers of vehicles thatmay engage in improper or unlawful activity. For example, lawenforcement authorities have set up stationary traffic cameras tophotograph the license plates of vehicles that may be traveling above aposted speed limit at a specific portion of a road or vehicles thatdrive through red lights. Toll booth operators also commonly use suchstationary cameras to photograph vehicles that may pass through a tollbooth without paying the required toll. However, all of these scenarioshave a common thread. The camera must be manually installed andconfigured such that it will always photograph the vehicle's licenseplate at a specific angle and when the vehicle is in a specificlocation. Any unexpected modifications, such as a shift in angle orlocation of the camera would render the camera incapable of properlycollecting license plate images.

Additionally, camera equipped mobile apparatuses (e.g., smartphones)have become increasingly prevalent in today's society. Mobileapparatuses are frequently used to capture optical images and for manyusers serve as a replacement for a simple digital camera because thecamera equipped mobile apparatus provides an image that is often as goodas those produced by simple digital cameras and can easily betransmitted (shared) over a network.

The positioning constraints put on the traffic cameras make it difficultto take images of license plates from different angles and distances andstill achieve an accurate reading. Therefore, it would be difficult toscale the same license plate image capture process performed by lawenforcement authorities to mobile apparatuses. In other words, it isdifficult to derive license plate information from an image of a licenseplate taken from a mobile image capture apparatus at a variety ofangles, distances, ambient conditions, mobile apparatus motion, and whenother object images are also in the image, which hinders a user'sability to easily gather valuable information about specific vehicleswhen engaging in a number of different vehicle related activities suchas buying and selling vehicles, insuring vehicles, and obtainingfinancing for vehicles.

SUMMARY

Several aspects of the present invention will be described more fullyhereinafter with reference to various methods and apparatuses.

Some aspects of the invention relate to a mobile apparatus including animage sensor configured to convert an optical image into an electricalsignal. The optical image includes an image of a vehicle license plate.The mobile apparatus includes a license plate detector configured toprocess the electrical signal to recover information from the vehiclelicense plate image. The mobile apparatus includes an interfaceconfigured to transmit the vehicle license plate information to a remoteapparatus and receive a vehicle financing offer in response to thetransmission.

Other aspects of the invention relate to a mobile apparatus including animage sensor configured to convert an optical image into an electricalsignal. The optical image includes several object images. One of theobject images includes a vehicle license plate image. The mobileapparatus includes a license plate detector configured to process theelectrical signal to recover information from the vehicle license plateimage from a portion of the electrical signal corresponding to said oneof the object images. The mobile apparatus includes an interfaceconfigured to transmit the vehicle license plate information to a remoteapparatus and receive a vehicle financing offer in response to thetransmission.

Other aspects of the invention relate to a mobile apparatus including animage sensor configured to convert an optical image into an electricalsignal. The mobile apparatus includes a display. The mobile apparatusincludes a rendering module configured to render the optical image tothe display. The mobile apparatus includes an image filter configured toapply one or more filter parameters to the electrical signal based on atleast one of color temperature of the image, ambient light, and motionof the apparatus. The mobile apparatus includes a license plate detectorconfigured to process the electrical signal to recover information fromthe vehicle license plate image. The rendering module is furtherconfigured to overlay a detection indicator on the displayed image toassist the user position of the apparatus with respect to the opticalimage in response to a signal from the image filter. The renderingmodule is further configured to provide an alert to the display when thelicense plate detector fails to recover the vehicle license plateinformation. The mobile apparatus includes an interface configured totransmit the vehicle license plate information to a remote apparatus andreceive a vehicle financing offer in response to the transmission.

Other aspects of the invention relate to a computer program product fora mobile apparatus having an image sensor configured to convert anoptical image into an electrical signal. The optical image includesseveral object images. One of the object images includes an image of avehicle license plate. The computer program product includes a machinereadable medium including code to process the electrical signal toselect said one of the object images. The machine readable mediumincludes code to process a portion of the electrical signalcorresponding to the selected said one of the object images to recoverinformation from the vehicle license plate image. The machine readablemedium includes code to transmit the vehicle license plate informationto a remote apparatus. The machine readable medium includes code toreceive a vehicle financing offer in response to the transmission.

Other aspects of the invention relate to a mobile apparatus including animage sensor configured to convert an optical image into an electricalsignal. The optical image includes an image of a vehicle license plate.The mobile apparatus includes a timing circuit configured to sample theelectrical signal at a frame rate. The mobile apparatus includes alicense plate detector configured to process the sampled electricalsignal to recover information from the vehicle license plate image. Themobile apparatus includes an interface configured to transmit thevehicle license plate information to a remote apparatus and receive avehicle financing offer in response to the transmission.

It is understood that other aspects of methods and apparatuses willbecome readily apparent to those skilled in the art from the followingdetailed description, wherein various aspects of apparatuses and methodsare shown and described by way of illustration. As understood by one ofordinary skill in the art, these aspects may be implemented in other anddifferent forms and its several details are capable of modification invarious other respects. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects of processes and apparatuses will now be presented inthe detailed description by way of example, and not by way oflimitation, with reference to the accompanying drawings, wherein:

FIG. 1 conceptually illustrates an exemplary embodiment of an apparatusthat is capable of capturing an optical image and detecting a licenseplate image from the optical image.

FIG. 2 illustrates an exemplary embodiment transmitting license plateinformation derived from an optical image to an external server.

FIG. 3 illustrates an exemplary embodiment of an apparatus fordisplaying a vehicle financing offer from a license plate image.

FIG. 3a illustrates an exemplary embodiment of an apparatus fordisplaying a list of available vehicles based on the financing offer.

FIG. 4 conceptually illustrates an exemplary embodiment of a process ofobtaining a vehicle financing offer from an optical image.

FIGS. 5a and 5b conceptually illustrate an exemplary embodiment of aprocess of obtaining a vehicle financing offer from a video.

FIG. 6 illustrates an exemplary embodiment of a system architecture of alicense plate detection apparatus.

FIG. 7 illustrates an exemplary embodiment of a diagram of the formatconverter.

FIG. 8 illustrates an exemplary embodiment of a diagram of the imagefilter.

FIG. 9 illustrates an exemplary embodiment of a diagram of a licenseplate detector.

FIG. 10 illustrates an exemplary embodiment of an object image with aconvex hull fit around the object image.

FIG. 11 illustrates an exemplary embodiment of a method for forming aquadrilateral from a convex hull.

FIG. 12 illustrates an exemplary embodiment of an object image enclosedin a quadrilateral.

FIG. 13 illustrates an exemplary embodiment of a diagram of therendering module.

FIG. 14 illustrates an exemplary embodiment of a scene that may becaptured by a license plate detection apparatus.

FIG. 15 provides a high level illustration of an exemplary embodiment ofhow an image may be rendered on a mobile apparatus by the license platedetection apparatus and transmission of a detected license plate imageto a server.

FIG. 16 conceptually illustrates an exemplary embodiment of a moredetailed process for processing an electrical signal to recover licenseplate information.

FIG. 17 illustrates an exemplary embodiment of an object imagecomprising a license plate image within a rectangle.

FIG. 18 illustrates an exemplary embodiment of an object imagecomprising a license plate image within a quadrilateral.

FIG. 19 is an illustration of an exemplary embodiment of the dewarpingprocess being performed on a license plate image.

FIG. 20 conceptually illustrates an exemplary embodiment of a processfor processing an optical image comprising a license plate image.

FIG. 21 illustrates an exemplary embodiment of a diagram for determiningwhether a patch is an actual license plate image.

FIG. 22 conceptually illustrates an exemplary embodiment of a processfor processing a patch comprising a candidate license plate image.

FIG. 23 illustrates an exemplary embodiment of vehicle financeunderwriting data flow.

FIG. 24 illustrates an exemplary embodiment of an operating environmentfor communication between a gateway and client apparatuses.

FIG. 25 illustrates a detailed exemplary embodiment of data flow betweena gateway and various other modules.

FIG. 26 conceptually illustrates an exemplary embodiment of a processfor transmitting a vehicle finance offer from a license plate image.

FIG. 27 illustrates an exemplary embodiment of an electronic system thatmay implement the license plate detection apparatus.

DETAILED DESCRIPTION

The detailed description set forth below in connection with the appendeddrawings is intended as a description of various exemplary embodimentsof the present invention and is not intended to represent the onlyembodiments in which the present invention may be practiced. Thedetailed description includes specific details for the purpose ofproviding a thorough understanding of the present invention. However, itwill be apparent to those skilled in the art that the present inventionmay be practiced without these specific details. In some instances,well-known structures and components are shown in block diagram form inorder to avoid obscuring the concepts of the present invention. Acronymsand other descriptive terminology may be used merely for convenience andclarity and are not intended to limit the scope of the invention.

The word “exemplary” or “embodiment” is used herein to mean serving asan example, instance, or illustration. Any embodiment described hereinas “exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments. Likewise, the term “embodiment” ofan apparatus, method or article of manufacture does not require that allembodiments of the invention include the described components,structure, features, functionality, processes, advantages, benefits, ormodes of operation.

It will be further understood that the terms “comprises” and/or“comprising,” when used in this specification, specify the presence ofstated features, steps, operations, elements, and/or components, but donot preclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof. The term “and/or” includes any and all combinations of one ormore of the associated listed items.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by aperson having ordinary skill in the art to which this invention belongs.It will be further understood that terms, such as those defined incommonly used dictionaries, should be interpreted as having a meaningthat is consistent with their meaning in the context of the relevant artand the present disclosure and will not be interpreted in an idealizedor overly formal sense unless expressly so defined herein.

In the following detailed description, various aspects of the presentinvention will be presented in the context of apparatuses and methodsfor recovering vehicle license plate information from an image. However,as those skilled in the art will appreciate, these aspects may beextended to recovering other information from an image. Accordingly, anyreference to an apparatus or method for recovering vehicle license plateinformation is intended only to illustrate the various aspects of thepresent invention, with the understanding that such aspects may have awide range of applications.

FIG. 1 conceptually illustrates an exemplary embodiment of an apparatus130 that is capable of capturing an optical image and detecting alicense plate 120 from the optical image. The apparatus 130 may be amobile phone, personal digital assistants (PDA), smart phone, laptopcomputer, palm-sized computer, tablet computer, game console, mediaplayer, digital camera, or any other suitable apparatus. FIG. 1 includesa vehicle 110, the license plate 120 registered to the vehicle 110, theapparatus 130, touch screen 140, and a user 150. The apparatus 130, ofsome embodiments, may be a wireless handheld device with built in imagecapture capabilities such as the smart phone, tablet or personal dataassistant (PDA) described above. However, in some aspects of theservice, the apparatus 130 may be a digital camera capable of processingor transferring data derived from the captured image to a personalcomputer. The information may then be uploaded from the personalcomputer to the license plate detection apparatus discussed in theforegoing.

In an exemplary embodiment of the apparatus, a customized application isinstalled on the apparatus 130. The customized application may interfacewith the apparatus' image capture device to capture an optical image,convert the optical image to an electrical signal, process theelectrical signal to detect the presence of a license plate image, andderive license plate information from a portion of the electrical signalthat is associated with the license plate image. The license plateinformation may be transmitted wirelessly to a server for furtherprocessing or decoding such as optical character recognition (OCR) ofthe license plate image. Alternatively, the OCR process may be carriedout on the mobile apparatus 130.

As shown in FIG. 1, the apparatus 130 may receive an interaction fromthe user 150 to capture an optical image that includes an object imageof the license plate 120. The interaction may occur at the touch screen140. The touch screen 140 shows an exemplary rendering of the opticalimage, including a rendering of the license plate image that may becaptured by the apparatus 130. As illustrated on the touch screen 140,the image of the license plate 120 may include a number and a state. OCRsoftware may be used to convert the state and number portions of thelicense plate image to text, which may be stored as strings to be usedlater for various functions. Once, a suitable image including a licenseplate image is captured by the apparatus 130, the license plate data maybe recovered and transmitted to a server for further processing.Additionally, the server and/or the apparatus application may provideerror checking capability to ensure that the captured image is clearenough to accurately detect and decode a license plate image. When theserver or apparatus determines that a suitable image has not beencaptured, the apparatus 130 may display an alert in the display area140, which may guide the user to acquiring a suitable image.

Alternatively, some aspects of the apparatus may provide the capabilityto bypass the image capture process to instead provide a user interfacewith text fields. For example, the user interface may provide textfields that allow for entry of the license plate number and state. Theentered information may be provided as text strings to the license platedetection apparatus without going through the detection processdiscussed above.

FIG. 2 illustrates an exemplary embodiment of transmitting license plateinformation derived from an optical image to an external server 230. Insome aspects of the apparatus, a license plate image may be transmittedafter recovering the license plate information from an image 220. Asshown, FIG. 2 includes an apparatus 210, the image 220, the server 230,and the Internet 240. The apparatus 210 may be a mobile or wirelessapparatus. The image 220 may be the same as the image rendered on thedisplay area 140 as illustrated in FIG. 1.

A license plate image recovered from the image 220 may be transmittedover the internet 240 to the server 230 where it is processed for thepurpose of detecting whether the license plate image is suitable forderiving license plate data and/or for performing OCR on the licenseplate image to derive license plate information such as the state oforigin and the license plate number.

Once the license plate image (or image file) is transmitted to theserver 230, the apparatus 210 may receive and display a confirmationmessage for confirming that the derived license plate information (e.g.,state and license plate number) is correct. In some aspects of theapparatus, the apparatus 210 may also display information about thevehicle to help the user determine whether the derived license plateinformation is correct. This may be useful in cases such as when theapparatus 210 captures a license plate image of a moving vehicle. Thevehicle license plate may no longer be in eyesight. However, it may bepossible to determine with some degree of accuracy whether the derivedlicense plate information is correct based on the vehicle informationthat is displayed on the mobile apparatus.

FIG. 3 illustrates an exemplary embodiment of an apparatus 300 fordisplaying a vehicle financing offer from a license plate image. Theapparatus 300 may be a handheld wireless device. The apparatus 300includes a display area 310. The display area 310 includes license plateinformation 320, selectable user interface (UI) object 335, slidableobjects 325, licensed driver information 360, finance offer 315, andloan details 345, 355, and 365. Each of the loan details 345, 355, and365 may be adjustable according to interaction from a user with theapparatus 300. FIG. 3 illustrates two stages 301-302 of a user'sinteraction with the apparatus 300.

In the first stage 301, the apparatus 300 may have transmitted a licenseplate image to the server 230 for further processing. Such processingwill be described in the foregoing figures. The apparatus 300 maydisplay the license plate information 320 and the licensed driverinformation 360. The licensed driver information may be captured from asecond image of a driver's license. For instance, after receiving thelicense plate image, the apparatus may request that the user provide animage of a driver's license. The apparatus may recognize informationfrom the driver's license such as state, number, name, and address. Suchinformation may be used in loan underwriting process. Additionally, therecognized information may be converted to text by OCR software. In thisexemplary illustration, the name recognized from the driver's licensemay be displayed in the display area 310 as licensed driver information360 for verification by the user. Once the user has verified that theinformation in the display area 310 matches up with the vehicleassociated with the license plate image and driver's licenseinformation, the apparatus 300 may receive a selection of the selectableUI object 335. However, in some aspects of the apparatus if thedisplayed information is not accurate, the apparatus 300 may receive aninteraction that may cause the apparatus 300 to prompt the user toretake the license plate image and/or the driver's license image. Insome embodiments of the apparatus, each selectable UI object may be aselectable button that is enabled when a user performs a gesturalinteraction with the touch screen of the apparatus 300.

In the second stage 302, the apparatus 300 may have received a selectionof the selectable UI object 335. In response, the display area 310 maypresent the financing offer 315, adjustable loan details 345, 355, and365, and slidable objects 325. The loan details may include a downpayment 345, a monthly payment 355, and a term 365. As shown, thefinancing offer 315 may include an annual percentage rate (APR) and aloan amount. In some aspects of the apparatus, the loan amount may bederived by obtaining a vehicle value from the license plate image.Alternatively, the loan amount may be obtained by user input, or as willbe discussed in the following figure, based on a particular vehiclelisting. The APR may be based on several factors used to determine therisk of funding a loan to the user of the apparatus 300.

As shown, one of the slidable objects 325 may be moved by receiving agestural interaction from a user. As each of the slidable objects move,the finance offer 315 may change as well as the various loan details345, 355, and 365. For instance, if the slidable object 325 associatedwith the down payment 345 is adjusted to lower the down payment, the APRof the financing offer 315 may increase as well as the monthly payment355. Alternatively or conjunctively, if the slidable object 325associated with the loan term 365 is adjusted to lower the term, the APRassociated with the finance offer 315 may decrease, while the monthlypayment 355 may increase. Thus, when at least one slidable object 325 ismoved, the loan details 345, 355, 365, and the financing offer 315 mayadjust accordingly. Additionally, the slidable objects 325 mayautomatically adjust to correspond with the changes to the loan details345, 355, and 365.

Furthermore, the apparatus may have the capability of receiving severaldifferent financing offers from several different agencies. Financeoffer 315 may only represent the best offer received by the apparatus.However, if the user wishes to see all of the financing offers, theapparatus 300 may receive a gestural interaction to bring up ascrollable display of all financing offers (not shown). Such offers maybe shown based on APR and/or qualifying loan amount.

Once the loan details and finance terms are worked out, a loan may beunderwritten and funded in near real-time from information obtainedbased on the license plate image and the driver's license image. Thus,the apparatus 300 provides a simple and efficient way to finance avehicle by simply taking a photograph of a vehicle license plate.

FIG. 3a illustrates an exemplary embodiment of the apparatus 300 fordisplaying a list of available vehicles based on the financing offer. Inthis exemplary illustration the display area 310 includes a list ofvehicles 350 that may be similar to the vehicle associated with thelicense plate information shown in FIG. 3. Additionally, the list ofvehicles may be filtered based on the finance offer illustrated in thesecond stage 302 of FIG. 3. For instance, the list of vehicles 350 maybe filtered to only include vehicles that can be purchased forapproximately the monthly payment set in the second stage 302 of FIG. 3.Moreover, the listing of vehicles may be filtered based on the maximumloan amount that the user may qualify for or any of the other adjustableloan details shown in the second stage 302 of FIG. 3.

FIG. 4 conceptually illustrates an exemplary embodiment of a process 400of obtaining a vehicle finance offer from an optical image. The process400 may be performed by a mobile apparatus such as the apparatus 130described with respect to FIG. 1. The process 400 may begin after animage capture capability or an application is initiated on the mobileapparatus. In some aspects of the process, the application may enablethe image capture feature on the mobile apparatus.

As shown, the process 400 captures (at 410) an optical image thatincludes a vehicle license plate image. As will be discussing in thefollowing figure, some aspects of the apparatus may process a video. Aframe may then be extracted and converted to an image file.

At 420, the process 400 converts the optical image into an electricalsignal. The process 400 then processes (at 430) the electrical signal torecover license plate information. The process 400 determines (at 440)whether the license plate information was successfully recovered. Whenthe license plate information was successfully recovered, the process400 transmits (at 460) the license plate information to a remote server.The process 400 then receives (at 470) a financing offer correspondingto the vehicle license plate. The process 400 then ends. In some aspectsof the process, several different finance offers may be received. Suchoffers may be available for display at the mobile apparatus for the userof the mobile apparatus to interact with and view.

Returning to 440, when the process 400 determines that the license plateinformation was not successfully recovered, the process 400 displays (at450) an alert that the license plate information was not recovered. Insome aspects of the process, a message guiding the user to position themobile apparatus to achieve greater chances of recovering the licenseplate information may be provided with the displayed alert. The processthen ends. However, in some aspects of the process, rather than end, theprocess may optionally return to capture (at 410) another optical imageand repeat the entire process 400.

FIGS. 5a and 5b conceptually illustrate an exemplary embodiment of aprocess 500 of obtaining a vehicle finance offer from a video. Theprocess 500 may be performed by a mobile apparatus such as the apparatus130 described with respect to FIG. 1. The process 500 may begin after animage and/or video capture capability or an application is initiated onthe mobile apparatus. The application may enable the image and/or videocapture feature on the mobile apparatus.

As shown, the process 500 converts (at 505) an optical image into anelectrical signal for sampling the electrical signal at n frames/second(fps). In some aspects of the process, the process may sample theelectrical signal at intervals such as 24 fps or any other suitableinterval for capturing video according to the apparatus' capabilities.Each sample of the electrical signal represents a frame of a video imagepresented on a display. The process 500 samples (at 510) a first portionof the electrical signal representing a first frame of the video imagepresented on the display. The process then determines (at 515) whetherany object image(s) are detected within the frame. At least one of thedetected object image(s) may comprise a license plate image. When theprocess 500 determines that at least object image exists within theframe, the process 500 assigns (at 520) a score based on the detectedobject image. The score may be based on the likelihood that at least oneof the object images is a license plate image and is discussed ingreater detail below with respect to FIGS. 21 and 22. The score may beapplied to each object image and/or aggregated for each object imagedetected in the frame. The process may then store (at 525) the score andassociated object image and frame information in a data structure. Insome aspects of the process, the process 500 may store the aggregatedobject image score and/or the process 500 may store the highest scoringobject image in the frame.

When the process 500 determines (at 515) that no object image existswithin the frame or after the process 500 stores the score (at 525), theprocess 500 displays feedback to a user based on the object imagedetected (or not detected). For instance, when no object image isdetected in the frame, the process 500 may display a message guiding theuser on how to collect a better optical image. However, when at leastone object image is detected in the frame, the process 500 may providefeedback by overlaying rectangles around the detected object image(s).Alternatively or conjunctively, the process 500 may overlay a rectanglethat provides a visual cue such as a distinct color, indicating whichobject image is determined to most likely be a license plate image orhas a higher score than other object images within the frame. In someaspects, the visual cue may be provided when a particular object imagereceives a score above a threshold value.

The process 500 optionally determines (at 535) whether user input hasbeen received to stop the video. Such user input may include a gesturalinteraction with the mobile apparatus, which deactivates the camerashutter on the mobile apparatus. When the process 500 determines (at535) that user input to stop the video capture is received, the process500 selects (at 545) the highest scoring frame according to the storedframe information. When the process 500 determines (at 535) that userinput to stop the video capture has not been received, the process 500determines (at 540) whether to sample additional portions of theelectrical signal. In some aspects of the process, such a determinationmay be based on a predetermined number of samples. For instance, themobile apparatus may have a built in and/or configurable setting for thenumber of samples to process before a best frame is selected. In otheraspects of the process, such a determination may be based on achieving ascore for a frame or object image in a frame that is above apredetermined threshold value. In such aspects, the frame or framecomprising the object image that is above the threshold score will beselected (at 545). When process 500 determines that there are moreportions of the electrical signal to be sampled, the process 500 samples(at 550) the next portion of the electrical signal representing the nextframe of the video image presented on the display. The process 500 thenreturns to detect (at 515) object image(s) within the next frame. Insome aspects of the process, the process may receive user input to stopthe video capture at any point while process 500 is runningSpecifically, the process is not confined to receiving user input tohalt video capture after the feedback is displayed (at 530); the userinput may be received at anytime while the process 500 is running. Insuch aspects, if at least one object image has been scored, then theprocess 500 will still select (at 545) the highest scoring object image.However, if no object images were scored, then the process will simplyend.

In some aspects of the process, the process 500 may optionally use theobject image(s) detected in the previous sample to estimate thelocations of the object images in the sample. Using this approachoptimizes processing time when the process can determine that the mobileapparatus is relatively stable. For instance, the mobile apparatus mayconcurrently store gyro accelerometer data. The process 500 may then usegyro accelerometer data retrieved from the mobile apparatus to determinewhether the mobile apparatus has remained stable and there is a greaterlikelihood that the object image(s) will be in similar locations. Thus,when the process 500 can determine that the mobile apparatus isrelatively stable, the processing time for license plate detection maybe increased because less of the portion of the electrical signal thatrepresents the video image would need to be searched for the licenseplate image.

Alternatively or conjunctively, the process 500 may not use informationabout object image(s) from the previous frame as a predictor. Instead,the process 500 may undergo the same detection and scoring processdiscussed above. Then, for each object image that overlaps an objectimage detected in a previous frame (e.g., the object images sharesimilar pixels either by space and/or location in the frames), theprevious frame receives a higher score. Information about theoverlapping object image(s) may be maintained for optimized processinglater on. Additionally, in some aspects of the apparatus, the licenseplate detection apparatus may maintain a table of matching objectimage(s) for the sampled portions of the electrical signal representingframes of video images over time. In such aspects, some object image(s)may exist in one or a few of the frames or some may exist in many or allframes and accordingly with higher scores. In such instances, all of theoverlapping object images may be processed as discussed in greaterdetail in the foregoing sections and provided to the server for OCR oridentification. This would lead to greater accuracy in actual licenseplate detection and OCR results.

Returning now to FIGS. 5a and 5b , after selecting (at 545) the highestscoring frame, the process 500 processes (at 555) the electrical signalbased on the information associated with the selected frame to recoverlicense plate information. The process 500 then determines (at 560)whether license plate information was recovered from the electricalsignal. When the process 500 determines (at 560) that license plateinformation has been recovered, the process 500 transmits (at 570) thelicense plate information to a remote server. The process 500 thenreceives (at 575) a financing offer corresponding to the vehicle licenseplate. The process 500 then ends. In some aspects of the process,several different finance offers may be received. Such offers may beavailable for display at the mobile apparatus for the user of the mobileapparatus to interact with and view.

Returning to 560, when the process 500 determines that license plateinformation was not detected, the process 500 displays (at 565) an alertthat license plate information cannot be recovered. Such an alert mayguide the user to acquiring better video that is more likely to producea readable license plate image. For instance, the alert may guide theuser's mobile device position or angle. The process 500 may then returnto collect additional video.

FIG. 6 illustrates an exemplary embodiment of a system architecture of alicense plate detection apparatus. The plate detection apparatus may bea mobile apparatus such as the apparatus 130 described with respect toFIG. 1 or any other suitable mobile apparatus that has image capture andprocessing capabilities.

The license plate detection apparatus includes an image captureapparatus 605, an imager 610, a keypad 615, a strobe circuit 685, aframe buffer 690, a format converter 620, an image filter 625, a licenseplate detector 630, a network 635, network interfaces 640 and 697, agateway 645, a rendering module 650, and a display 655. The licenseplate detection apparatus may communicate with a server having OCRModule 660, and an OCR analytics storage 670. However, in some aspectsof the apparatus, the OCR module and/or OCR analytics storage may bepart of the mobile apparatus. The license plate detection apparatusillustrated in FIG. 6 generates license plate information 675, which maybe processed by various modules communicatively coupled to the gateway.The processed information may then be used by the vehicle financingoffer generator 695. The vehicle financing quote generator 695 may beused to obtain a finance offer from a license plate image. In someaspects of the apparatus, the vehicle financing offer generator 695 maybe tied to a loan underwriting service accessible via an API. In suchaspects, when a device receives all the requisite criteria for obtaininga financing offer for a vehicle based on a license plate image, thevehicle financing offer generator 695 may communicate with the financeunderwriting service to underwrite the user for a loan in near real timebased on the license plate image.

As shown, the image capture apparatus 605 communicates an optical imageto the imager 610. The image capture apparatus 605 may comprise a cameralens and/or a camera that is built into a mobile apparatus. The imager610 may comprise a CMOS array, NMOS, CCD, or any other suitable imagesensor that converts an optical image into an electrical signal (e.g.,raw image data). The electrical signal comprises pixel data associatedwith the captured image. The amount of pixel data is dependent on theresolution of the captured image. The pixel data is stored as numericalvalues associated with each pixel and the numerical values indicatecharacteristics of the pixel such as color and brightness. Thus, theelectrical signal comprises a stream of raw data describing the exactdetails of each pixel derived from the optical image. During the imagecapture process, the imager 610 may produce a digital view as seenthrough the image capture apparatus for rendering at the display 655.

In some aspects of the apparatus, the image capture apparatus 605 may beconfigured to capture video. In such aspects, a timing circuit, such asthe strobe circuit 685, may communicate with the imager 610. The strobecircuit 685 may sample (or clock) the imager 610 to produce a sampledelectrical signal at some periodicity such as 24-30 fps. The sampledelectrical signal may be representative of a frame of video presented onthe display 655. The electrical signal may be provided to the framebuffer 690. However, the imager 610 may communicate the electricalsignal directly to the format converter 620 when a single optical imageis captured. When video is captured, the frame buffer may communicatethe sample of the electrical signal representative of the frame of videofrom the frame buffer to the format converter 620. However, in someaspects of the apparatus, the frame buffer 690 may be bypassed such thatthe sampled electrical signal is communicated directly to the formatconverter 620.

The format converter 620 generates or compresses the raw image pixeldata provided in the electrical signal to a standard, space efficientimage format. However, in some aspects of the apparatus, the framebuffer 690 and format converter 620 may be reversed such that thesampled electrical signals are converted to a compressed standard videoformat before buffering. The standard image and/or video format can beread by the following modules of the license plate detection apparatus.However, the following description will assume that the sampledelectrical signals are buffered before any such format conversion. Theformat converter 620 will be described in greater detail in FIG. 7.

The format converter 620 communicates the standard image file (or image)to the image filter 625. The image filter 625 performs a variety ofoperations on the image to provide the optimal conditions to detect alicense plate image within the image. Such operations will be describedin greater detail in FIG. 8. However, if the image filter 625 determinesthat the image is too distorted, noisy, or otherwise in a condition thatis unreadable to the point that any filtering of the image will notresult in a viable image for plate detection, the image filter 625 willsignal to the rendering module 650 to display an alert on the display655 that the image is not readable. Alternatively, once the image isfiltered, ideally the image should be in a state that is optimal foraccurate license plate detection. Therefore, the image filter 625 willthen communicate the filtered image to the license plate detector 630.

The plate detector 630 is an integral module of license plate detectionapparatus. The plate detector 630 will process the image to detect thepresence of a license plate image by implementing several processeswhich will be described in greater detail in FIG. 9. The license platedetector may generate overlays such as rectangular boxes around objectimages it detects as potential or candidate license plate images. Theoverlays may be transmitted as signals from the license plate detector630 to the rendering module 650. The rendering module may instruct thedisplay 655 to display the overlays over the image received from theimager 610 so that the user of the mobile apparatus can receive visualguidance relating to what object images the license plate detectionapparatus detects as candidate license plate images. Such information isuseful in guiding the user to capture optical images that include thelicense plate image and provide a higher likelihood of accurate licenseplate information recovery.

The license plate detector 630 will determine which portion of the image(or electrical signal) is most likely a license plate image. The licenseplate detector 630 will then transmit only the license plate imageportion of the image to the network 635 by way of the network interface697. Alternatively, a user may skip the entire image conversion processand using the keypad 615, key in the license plate information, which isthen transmitted over the network 635 by way of the network interface697. The network 635 then transmits the license plate image information(or image file) or keyed information to the network interface 640, whichtransmits signals to the gateway 645.

The gateway 645 may transmit the license plate image data to the OCRmodule 660. The OCR module 660 derives the license plate informationsuch as state and number information from the license plate image. TheOCR module 660 may use several different third party and/or proprietaryOCR applications to derive the license plate information. The OCR module660 may use information retrieved from the OCR analytics storage 670 todetermine which OCR application has the greatest likelihood of accuracyin the event that different OCR applications detected differentcharacters. For instance, the OCR analytics storage 670 may maintainstatistics collected from the user input received at the apparatus 300described with respect to FIG. 3. The OCR module 660 may then select thelicense plate information that is statistically most likely to beaccurate using information retrieved from the analytics storage 670. TheOCR module 660 may then transmit the license plate information 675 as atext string or strings to the gateway 645, which provides the licenseplate information 675 to the rendering module 650 through the network635. The rendering module 650 may then instruct the display 655 todisplay the license plate information 675. The display 655 may thendisplay a message similar to the one described with respect to FIG. 3.

Additionally, the license plate information 675 may be transmittedthrough the gateway 645 and processed by various modules communicativelycoupled to the gateway 645. The gateway 645 may transmit the processedinformation to the vehicle financing offer generator 695. The vehiclefinancing offer generator 695 may communicate with at least third partyservice by way of an API to receive at least one financing offer basedon the license plate information. The financing offer may then betransmitted back to the gateway 645 for further processing.Alternatively, or in addition to, the gateway 645 may transmit thefinancing offer to the rendering module 650 through the network 635. Therendering module 650 may then instruct the display 655 to display thefinancing offer along with any other information to assist the user ofthe mobile apparatus.

In the event that the OCR module 660 or the license plate detector 630is unable to detect a license plate image or identify any license plateinformation, the OCR module 660 and/or the license plate detector 630will signal an alert to the rendering module 650, which will be renderedon the display 655.

In some aspects of the apparatus, the OCR module 660 may be located onan apparatus separate from an external server. For instance, the OCRmodule 660 may be located on the mobile apparatus 130 similar to thelicense plate detection apparatus. Additionally, in some aspects of theapparatus, the format converter 620, image filter 625, and license platedetector 630 may be located on an external server and the electricalsignal recovered from the optical image may be transmitted directly tothe network 635 for processing by the modules on the external server.

The license plate detection apparatus provide several advantages in thatit is not confined to still images. As discussed above, buffered orunbuffered video may be used by the license plate detection apparatus todetermine the frame with the highest likelihood of having a licenseplate image. It also enables optical images to be taken while a mobileapparatus is moving and accounts for object images recovered from anyangle and/or distance. Additionally, the license plate detectionapparatus also provides the added benefit of alerting the user when alicense plate image cannot be accurately detected in addition toguidance relating to how to get a better image that is more likely toproduce license plate information. Such guidance may include directionalguidance such as adjusting the viewing angle or distance as well asguidance to adjust lighting conditions, if possible. Thus, the licenseplate detection apparatus provides a solution to the complicated problemof how to derive license plate information captured from moving objectimages and from virtually any viewing angle. The license plateinformation may then be used to derive different information associatedwith the license plate information such an estimated value for avehicle.

FIG. 7 illustrates an exemplary embodiment of a diagram of the formatconverter 620. The format converter 620 receives the input of anelectrical signal that defines an image 720 or an electrical signal thatdefines a sequence of sampled images such as video frames 725. Theformat converter 620 outputs an image file 730 in a standard format suchas the formats discussed above with respect to FIG. 1. The formatconverter 620 includes a frame analyzer 715 and a conversion engine 710.When an electrical signal defining an image 720 is received at theformat converter 620, the electrical signal will be read by theconversion engine 710. The conversion engine 710 translates the pixeldata from the electrical signal into a standard, compressed image formatfile 730. Such standard formats may include .jpeg, .png, .gif, .tiff orany other suitable image format similar to those discussed with respectto FIG. 1. In the exemplary instance where the format converter 620converts video to a standard format, the standard format may include.mpeg, .mp4, .avi, or any other suitable standard video format. Sincethe electrical signal received at the format converter 620 is raw datawhich can make for a very large file, the conversion engine may compressthe raw data into a format that requires less space and is moreefficient for information recovery.

The format converter 620 may also receive a several sampled electricalsignals, each representing frames of video images, such as frame data725. The video data frames may be received at the frame analyzer 715 inthe format converter 620. The frame analyzer 715 may perform a number ofdifferent functions. For instance, the frame analyzer 715 may perform afunction of analyzing each frame and discarding any frames that areblurry, noisy, or generally bad candidates for license plate detectionbased on some detection process such as the process 500 described inFIG. 5. Those frames that are suitable candidates for license platedetection may be transmitted to the conversion engine 710 and convertedto the standard format image 730 similar to how the image 720 wasconverted.

FIG. 8 illustrates an exemplary embodiment of a diagram of the imagefilter 625. The image filter 625 receives a formatted image file thatthe image filter 625 is configured to read. The image filter 625 outputsa filtered image 840 which may be optimized for more reliable licenseplate recognition. Alternatively, if the image filter 625 determinesthat the image is unreadable, the image filter 625 may signal an alert845, indicating to the user that the image is unreadable and/or guidethe user to capture a better image.

The image filter 625 includes a filter processor 805, a grayscale filter810, and a parameters storage 835. When the image filter 625 receivesthe formatted image file 830, the filter processor 805 will retrieveparameters from the parameters storage 835, which will assist the filterprocessor 805 in how to optimally filter the image. For instance, if thereceived image was taken in cloudy conditions, the filter processor 805may adjust the white balance of the image based on the parametersretrieved from the parameters storage 835. If the image was taken in thedark, the filter processor 805 may use a de-noise function based on theparameters retrieved from the parameters storage 835 to remove excessnoise from the image. In some aspects of the apparatus, the filterprocessor 805 also has the ability to learn based on the success ofpreviously derived license plate images what parameters work best orbetter in different conditions such as those conditions described above.In such aspects, the filter processor 805 may take the learned data andupdate the parameters in the parameters storage 835 for future use.

The filter processor 805 also has logic to determine if an image will bereadable by the license plate detector 630. When the filter processor805 determines that the image will not be readable by the license platedetector 630, the filter processor 805 may signal an alert 845 to therendering module 650. However, when the filter processor 805 determinesthat sufficient filtering will generate a readable image for reliablelicense plate detection, the filter processor 805 communicates theimage, post filtering, to the grayscale filter 810.

Additionally, in some aspects of the apparatus, the image filter 625 mayreceive several images in rapid succession. Such instances may be framesof a video that may be captured while a mobile apparatus is moving. Insuch instances, the filter processor 805 may continuously adjust thefilter parameters to account for each video frame, it receives. The samealerts may be signaled in real-time in the event that a video frame isdeemed unreadable by the filter processor 805.

The grayscale filter 810 will convert the received image file tograyscale. More specifically, the grayscale filter will convert thepixel values for each pixel in the received image file 830 to new valuesthat correspond to appropriate grayscale levels. In some aspects of thefilter, the pixel values may be between 0 and 255 (e.g., 256 values or2⁸ values). In other aspects of the filter, the pixel values may bebetween 0 and any other value that is a power of 2 minus 1, such as1023, etc. The image is converted to grayscale, to simplify and/or speedup the license plate detection process. For instance, by reducing thenumber of colors in the image, which could be in the millions, to shadesof gray, the license plate image search time may be reduced.

In the grayscale image, regions with higher intensity values (e.g.,brighter regions) of the image will appear brighter than regions of theimage with lower intensity values. The grayscale filter 810 ultimatelyproduces the filtered image 840. However, one skilled in the art shouldrecognize that the ordering of the modules is not confined to the orderillustrated in FIG. 8. Rather, the image filter may first convert theimage to grayscale using the grayscale filter 810 and then filter thegrayscale image at the filter processor 805. The filter processor 805then outputs the filtered image 840. Additionally, it should be notedthat the image filter 625 and the format converter 620 may beinterchangeable. Specifically, the order in which this image isprocessed by these two modules may be swapped in some aspects of theapparatus.

FIG. 9 illustrates an exemplary embodiment of a diagram of the licenseplate detector 630. The license plate detector 630 receives a filteredimage 930 and processes the image to determine license plate information935, which is may be a cropped image of at least one license plateimage. The license plate detector 630 comprises an object detector 905,a quad processor 910, a quad filter 915, a region(s) of interestdetector 920, and a patch processor 925. The license plate detector 630provides the integral function of detecting a license plate image froman image at virtually any viewing angle and under a multitude ofconditions, and converting it to an image that can be accurately read byat least one OCR application.

The license plate detector 630 receives the filtered image 930 at theobject detector 905. As discussed above, the filtered image 930 has beenconverted to a grayscale image. The object detector 905 may use amathematical method, such as a Maximal Stable Extremal Regions (MSER)method, for detecting regions in a digital image that differ inproperties, such as brightness or color, compared to areas surroundingthose regions. Simply stated, the detected regions of the digital imagehave some properties that are constant or vary within a pre-describedrange of values; all the points (or pixels) in the region can beconsidered in some sense to be similar to each other. This method ofobject detection may provide greater accuracy in the license platedetection process than other processes such as edge and/or cornerdetection. However, in some instances, the object detector 905 may useedge and/or corner detection methods to detect object images in an imagethat could be candidate license plate images.

Typically, the object images detected by the object detector 905 willhave a uniform intensity throughout each adjacent pixel. Those adjacentpixels with a different intensity would be considered background ratherthan part of the object image. In order to determine the object imagesand background regions of the filtered image 930, the object detector905 will construct a process of applying several thresholds to theimage. Grayscale images may have intensity values between 0 and 255, 0being black and 255 being white. However, in some aspects of theapparatus, these values may be reversed with 0 being white and 255 beingblack. An initial threshold is set to be somewhere between 0 and 255.Variations in the object images are measured over a pre-determined rangeof threshold values. A delta parameter indicates through how manydifferent gray levels a region needs to be stable to be considered apotential detected object image. The object images within the image thatremain unchanged, or have little variation, over the applied deltathresholds are selected as likely candidate license plate images. Insome aspects of the detector, small variations in the object image maybe acceptable. The acceptable level of variations in an object image maybe programmatically set for successful object image detection.Conversely or conjunctively, the number of pixels (or area of the image)that must be stable for object image detection may also be defined. Forinstance, a stable region that has less than a threshold number ofpixels would not be selected as an object image, while a stable regionwith at least the threshold number of pixels would be selected as anobject image. The number of pixels may be determined based on knownvalues relating to the expected pixel size of a license plate image orany other suitable calculation such as a height to width ratio.

In addition, the object detector 905 may recognize certainpre-determined textures in an image as well as the presence ofinformative features that provide a greater likelihood that the detectedobject image may be a license plate image. Such textures may berecognized by using local binary patterns (LBP) cascade classifiers. LBPis especially useful in real-time image processing settings such as whenimages are being captured as a mobile apparatus moves around an area.Although commonly used in the art for image facial recognition, LBPcascade classifiers may be modified such that the method is optimizedfor the detection of candidate license plate images.

In an LBP cascade classification, positive samples of an object imageare created and stored on the license plate detection apparatus. Forinstance, a sample of a license plate image may be used. In someinstances multiple samples may be needed for more accurate object imagedetection considering that license plates may vary from state to stateor country to country. The apparatus will then use the sample objectimages to train the object detector 905 to recognize license plateimages based on the features and textures found in the sample objectimages. LBP cascade classifiers may be used in addition to theoperations discussed above to provide improved detection of candidatelicense plate images.

Once the object detector 905 has detected at least one object as acandidate license plate image, the object detector 905 will passinformation relating to the detected object images to the quad processor910 and/or the quad filter 915. In some aspects of the detector, theobject images may not be of a uniform shape such as a rectangle or oval.The quad processor 910 will then fit a rectangle around each detectedobject image based on the object image information provided by theobject detector 905. Rectangles are ideal due to the rectangular natureof license plates. As will be described in the foregoing, informationabout the rectangles may be used to overlay rectangles on object imagesthat are displayed for the user's view on a mobile apparatus.

The rectangle will be sized such that it fits minimally around eachobject image and all areas of the object image are within the rectanglewithout more additional background space than is necessary to fit theobject image. However, due to various factors such as the angle at whichthe optical image was taken, the license plate image may not beperfectly rectangular. Therefore, the quad processor 910 will perform aprocess on each object image using the rectangle to form a quadrilateralfrom a convex hull formed around each object image.

The quad processor 910 will use an algorithm that fits a quadrilateralas closely as possible to the detected object images in the image. Forinstance, the quad processor 910 will form a convex hull around theobject image. A convex hull is a polygon that fits around the detectedobject image as closely as possible. The convex hull comprises edges andvertices. The convex hull may have several vertices. The quad processor910 will take the convex hull and break it down to exactly four vertices(or points) that fit closely to the object image.

FIGS. 10-12 illustrate the functionality of the quad processor 910. Asshown, FIG. 10 illustrates an exemplary embodiment of an object image1005 with a convex hull 1010 fit around the object image 1005, and ablown up region 1015. The convex hull 1010 comprises several edges andvertices including vertices A-D. In order to fit the object image 1005into a quadrilateral, the convex hull 1010 may be modified such thatonly 4 vertices are used. For instance, as illustrated in FIG. 11, foreach adjacent pair of points A-D in the convex hull 1010, the quadprocessor 910 will find a new point Z that maintains convexity andenclosure of the object image when B and C are removed. Point Z ischosen as a point that provides a minimal increase to the hull area anddoes not go outside of the originally drawn rectangle (not shown). Thus,FIG. 11 illustrates an exemplary embodiment of a method for forming aquadrilateral (shown in FIG. 12) from the convex hull 1010. The processrepeats for each set of 4 points until the convex hull 1010 iscompressed to only four vertices as illustrated in FIG. 12.

FIG. 12 illustrates an exemplary embodiment of the object image 1005enclosed in a quadrilateral 1210. As shown in FIG. 12, Quadrilateral1210 fits as closely to the object image 1005 as possible without anyedge overlapping the object image 1005. Fitting a quadrilateral closelyto an object image as illustrated by the FIGS. 10-12 provides thebenefit of greater efficiency in the license plate detection process. Aswill be described below, the license plate detection apparatus will onlysearch the portions of the image within the quadrilaterals for thepresence of a license plate image.

Referring back to FIG. 9, now that the quad processor 910 has drawnefficient quadrilaterals around each of the detected object images, thecoordinates of the quadrilaterals are passed to the quad filter 915and/or the region(s) of interest detector 920. As discussed above, thelicense plate detection apparatus first overlays rectangles around eachdetected object image. The quad filter 915 may use the rectangleinformation (rather than the quadrilateral information) received fromthe object detector 905, such as the pixel coordinates of the rectanglesin the image, and look for rectangles similar in size and that overlap.The quad filter 915 will then discard the smaller rectangle(s), whilemaintaining the biggest. If at least two rectangles are of an identicalsize and overlap, the quad filter 915 will use a mechanism to determinewhich rectangle is more likely to be a full license plate image anddiscard the less likely image within the other rectangle(s). Suchmechanisms may involve textures and intensity values as determined bythe object detector 905. In some aspects of the filter, rather thansearching only rectangles, the quad filter 915 may alternatively oradditionally search the quadrilateral generated by the quad processor910 for duplicates and perform a similar discarding process. Byfiltering out the duplicates, only unique object images within therectangles will remain, with the likelihood that at least one of thoseobject images is a license plate image. Thus, at this point, the licenseplate detection apparatus will only need to search the areas within therectangles or quadrilaterals for the license plate image.

The region(s) of interest detector 920 will then determine which of theobject images are actually object images that that have similarproportions (e.g., height and width) to the proportions that would beexpected for a license plate image. For instance, typically a licenseplate is rectangular in shape. However, depending on several factorssuch as the angle that the license plate image was captured, the objectimage may appear more like a parallelogram or trapezoid. However, thereis a limit to how much skew or keystone (trapezoidal shape) a licenseplate image undergoes before it becomes unreadable. Therefore, it isnecessary to compute a skew factor and/or keystone to determine whetherthe object image may be a readable license plate image. Specifically,object images that have a skew factor and/or keystone below and/or abovea threshold value are likely object images that do not have theproportions expected for a license plate image or would likely beunreadable. Since a license plate has an expected proportion a thresholdskew factor and/or keystone may be set and any detected object imagethat has a skew factor and/or keystone indicating that the object imageis not a readable license plate image will be discarded. For instance,license plate images with a high skew and/or high keystone may bediscarded.

In some aspects of the apparatus, the skew and keystone thresholds maybe determined by digitally distorting known license plate images withvarying amounts of pitch and yaw to see where the identification processand/or OCR fails. The threshold may also be dependent on the size of theobject image or quadrilateral/trapezoid. Thus, quadrilaterals ortrapezoids must cover enough pixel space to be identified and read bythe OCR software. Those that do not have a large enough pixel space,skew factors that are too high, and/or keystones that are too high wouldthen be discarded as either being unlikely candidates for license plateimages or unreadable license plate images.

The skew factor is computed by finding the distance between opposingvertices of the quadrilateral and taking the ratio of the shorterdistance to the longer distance so that the skew factor is less than orequal to 1. Rectangles and certain parallelograms that are likelycandidate license plate images will have a skew factor that is close to0, while skewed parallelograms will have a high skew factor.Additionally, trapezoids that are likely candidate license plate imageswill have a keystone that is close to 0, while trapezoids that areunlikely candidate license plate images will have a high keystone.Therefore, object images with a high skew factor are discarded, whilethe parallelograms with a lower skew factor and trapezoids with a lowerkeystone are maintained. In some aspects of the apparatus, a thresholdskew and a threshold keystone may be defined. In such aspects,parallelograms having a skew factor below the threshold are maintainedwhile those above the threshold are discarded. Similarly, in suchaspects, trapezoids having a keystone below the threshold are maintainedwhile those above the threshold are discarded. When the value is equalto the threshold, the parallelogram or trapezoid may be maintained ordiscarded depending on the design of the apparatus.

The remaining parallelograms and trapezoids are then dewarped. Thedewarping process is particularly important for the trapezoids becauseit is used to convert the trapezoid into a rectangular image. Thedewarping process uses the four vertices of the quadrilateral and the 4vertices of an un-rotated rectangle with an aspect ratio of 2:1(width:height), or any other suitable license plate aspect ratio, tocomputer a perspective transform. The aspect ratio may be pixelwidth:pixel height of the image. The perspective transform is applied onthe region around the quadrilateral and the 2:1 aspect ratio objectimage is cropped out. The cropped object image, or patch, is an objectimage comprising a candidate license plate image.

The patch is then provided to the patch processor 925, which will searchfor alpha numeric characters in the patch, find new object images withinthe patch, fit rectangles around those object images, and compute ascore from the fit rectangles. The score may be based on a virtual linethat is drawn across the detected object images. If a line exists thathas a minimal slope, the object images on that line may receive a scorethat indicates the object image is highly likely to be a license plateimage. If no line with a minimal slope is detected, then an alert may bereturned to the rendering module that a license plate image was notdetected in the image. Scores may be calculated for several differentpatches from the same image and it follows that more than one licenseplate image may be detected in the same image. Once, the presence of alicense plate image is detected, the license plate information 935 maybe transmitted to a server for OCR and further processing. In someaspects of the apparatus, the license plate information is an image filecomprising the license plate image. Additionally, the process forscoring the patch will be described in more detail with respect to FIG.21.

FIG. 13 illustrates an exemplary embodiment of a diagram of therendering module 650. The rendering module 650 may receive as inputalert information from the image filter 1335, or information aboutdetected object images from the license plate detector 1330. Therendering module will then communicate rendering instructions 1340 tothe display 655. The rendering module 650 includes an overlay processor1305, a detection failure engine 1310, and an image renderer 1315.

The overlay processor 1305 receives information about the detectedobject images 1330 from the license plate detector 630. As discussedabove, such information may include coordinates of detected objectimages and rectangles determined to fit around those object images. Therectangle information is then provided to the detection failure engine1310, which will determine that object images have been detected by thelicense plate detector 630. The detection failure engine 1310 may thenforward the information about the rectangles to the image renderer 1315,which will provide rendering instructions 1340 to the display for howand where to display the rectangles around the image received from theimager 610. Such information my include pixel coordinates associatedwith the size and location of the rectangle and color information. Forinstance, if the license plate detector 630 determines that a detectedobject image is more likely to be an actual license plate image than theother detected object images, the rendering module 650 may instruct thedisplay 655 to display the rectangle around the more likely object imagein a way that is visually distinct from other rectangles. For instance,the rectangle around the object image more likely to be a license plateimage may be displayed in a different color than the other rectangles inthe display.

However, in some instances, the license plate detector 630 may notdetect any object images. In such instances, the overlay processor willnot forward any rectangle information to the detection failure engine1310. The detection failure engine 1310 will then determine there hasbeen an object image detection failure and signal an alert to the imagerenderer 1315. The image renderer 1315 will then communicate the displayrendering instructions 1340 for the alert to the display 655. Thelicense plate detection alerts have been described in greater detailabove.

Additionally, the image filter 625 may provide information to the imagerenderer 1315 indicating an alert that the captured image cannot beprocessed for some reason such as darkness, noise, blur, or any otherreason that may cause the image to be otherwise unreadable. The alertinformation from the image filter 625 is provided to the image renderer1315, which then provides the rendering display instructions 1340 to thedisplay 655 indicating how the alert will be displayed. The image filteralerts have been discussed in detail above.

The following FIGS. 14-22 provide exemplary illustrations and processesdetailing the functionality of the license plate detection module 630.FIGS. 14-22 are devised to illustrate how the license plate detectionapparatus goes from an optical image comprising many object images todetecting a license plate image among the object images.

FIG. 14 illustrates an exemplary embodiment of a scene 1400 that may becaptured by a mobile apparatus 1410. The mobile apparatus 1410 may besimilar to the mobile apparatus 130 described with respect to FIG. 1.The scene 1400 includes a structure 1425, a road 1430, a vehicle 1420,and a license plate 1405. The mobile apparatus 1410 includes a displayarea 1415.

As illustrated, the mobile apparatus 1410 has activated the imagecapture functionality of the mobile apparatus 1410. The image capturefunctionality may be an application that controls a camera lens andimager built into the apparatus 1410 that is capable of taking digitalimages. In some aspects of the apparatus, the image capturefunctionality may be activated by enabling an application whichactivates the license plate detection apparatus capabilities describedin FIG. 6. In this example, the mobile apparatus 1410 may be capturing astill image, several images in burst mode, or video, in real-time forprocessing by the license plate detection apparatus. For instance, thevehicle 1420 may be moving while the image capture process occurs,and/or the mobile apparatus may not be in a stationary position. In suchinstances, the license plate detection apparatus may determine the bestvideo frame taken from the video.

FIG. 15 provides a high level illustration of an exemplary embodiment ofhow an image may be rendered on an apparatus 1500 by the license platedetection apparatus and transmission of a detected license plate imageto a server 1550. As shown, the apparatus 1500 includes a display area1515 and an exploded view 1555 of the image that is rendered in displayarea 1515. The exploded view 1555 includes object images 1525,rectangles 1520 that surround the object images, overlapping rectangles1530, candidate license plate image 1505, and a rectangle 1510 thatsurrounds the candidate license plate image 1505. In the event that alicense plate image is detected and captured in the display area 1515,the apparatus 1500 may wirelessly transmit license plate image data 1535over the Internet 1540 to a server 1550 for further processing. In someaspects of the apparatus, the license plate image data may be an imagefile comprising a license plate image.

As shown in exploded view 1555, the object detector 905 of the licenseplate detection apparatus has detected several object images 1525, aswell as a candidate license plate image 1505. As shown, the renderingmodule 650 has used information communicated from the license platedetector 630 to overlay rectangles around detected object images 1525including the candidate license plate image 1505. The rendering module650 has also overlaid rectangles that differ in appearance around objectimages that are less likely to be license plate images. For instance,rectangles 1520 appear as dashed lines, while rectangle 1510 appears asa solid line. However, as those skilled in the art will appreciate, thevisual appearance of the rectangles is not limited to only thoseillustrated in exploded view 1555. In fact, the visual appearance of therectangles may differ by color, texture, thickness, or any othersuitable way of indicating to a user that at least one rectangle isoverlaid around an object image that is more likely to be a licenseplate image than the other object images in which rectangles areoverlaid.

Exploded view 1555 also illustrates overlapping rectangles 1530. Asdiscussed above, the quad filter 915 of the license plate detector 630may recognize the overlapping rectangles 1530 and discard some of therectangles, and detected object images within those discardedrectangles, as appropriate.

As is also illustrated by FIG. 15, the license plate detection apparatushas detected the presence of a candidate license plate image 1505 in theimage. As a result, the mobile apparatus 1500 will transmit the licenseplate image data 1535 associated with the license plate image over theinternet 1540 to the server 1550 for further processing. Such furtherprocessing may include OCR and using the license plate informationderived from the OCR process to perform a number of different tasks thatmay be transmitted back to the mobile apparatus 1500 for rendering onthe display area 1515. Alternatively, in some aspects of the apparatus,the OCR capability may be located on the mobile apparatus 1500 itself.In such aspects, the mobile apparatus may wirelessly transmit thelicense plate information such as state and number data rather thaninformation about the license plate image itself.

FIG. 16 conceptually illustrates an exemplary embodiment of a moredetailed process 1600 for processing an electrical signal to recoverlicense plate information as discussed at a high level in process 400.The process may also be applied to detecting license plate informationin a sample of an electrical signal representing a frame of a videoimage presented on a display as described in process 500 of FIG. 5. Theprocess 1600 may be performed by the license plate detection apparatus.The process 1600 may begin after the mobile apparatus has activated anapplication, which enables the image capture feature of a mobileapparatus.

As shown, the process 1600 converts (at 1610) the captured image tograyscale. As discussed above, converting the image to grayscale makesfor greater efficiency in distinguishing object images from backgroundaccording to the level of contrast between adjacent pixels. Severalfiltering processes may also be performed on the image during thegrayscale conversion process. The process 1600 then detects (at 1615)object image(s) from the grayscale image. Such object images may be theobject images 1505 and 1525 as illustrated in FIG. 15. The process 1600processes (at 1620) a first object image. The processing of objectimages will be described in greater detail in the foregoing description.

At 1625, the process 1600 determines whether an object image fits thecriteria for a license plate image. When the object image fits thecriteria for a license plate image, the process 1600 transmits (at 1630)the license plate image (or image data) to a server such as the server1550. In some aspects of the process, an object image fits the criteriafor a license plate when a score of the object image is above athreshold value. Such a score may be determined by a process which willbe discussed in the foregoing description. The process 1600 thendetermines (at 1635) whether there are more object images detected inthe image and/or whether the object image being processed does notexceed a threshold score.

When the process 1600 determines (at 1625) that an object image does notfit the criteria for a license plate image, the process 1600 does nottransmit any data and determines (at 1635) whether more object imageswere detected in the image and/or whether the object image beingprocessed did not exceed a threshold score. When the process 1600determines that more object images were detected in the image and/or theobject image being processed did not exceed a threshold score, theprocess 1600 processes (at 1640) the next object image. The process thenreturns to 1625 to determine if the object image fits the criteria of alicense plate image.

When the process 1600 determines (at 1635) that no more object imageswere detected in the image and/or the object image being processedexceeds a threshold score, the process 1600 determines (at 1645) whetherat least one license plate image was detected in the process 1600. Whena license plate image was detected, the process ends. When a licenseplate image was not detected, an alert is generated (at 1650) and therendering module 650 sends instructions to display a detection failuremessage at the display 655. In some aspects of the process, thedetection failure alert may provide guidance to the user for capturing abetter image. For instance, the alert may guide the user to move themobile apparatus in a particular direction such as up or down and/oradjust the tilt of the mobile apparatus. Other alerts may guide the userto find a location with better lighting or any other suitable messagethat may assist the user such that the license plate detection apparatushas a greater likelihood of detecting at least one license plate imagein an image.

The process 1600 may be performed in real-time. For instance, theprocess 1600 may be performed successively as more images are capturedeither by capturing several frames of video as the mobile apparatus orobject images in the scene move and/or are tracked or by using an imagecapture device's burst mode. The process 1600 provides the advantage ofbeing able to detect and read a license plate image in an image atvirtually any viewing angle and under a variety of ambient conditions.Additionally, the criteria for determining a license plate image isdetermined based on the operations performed by the license platedetector. These operations will be further illustrated in the followingfigures as well.

FIGS. 17-19 illustrate the operations performed by the quad processor910. For instance, FIG. 17 illustrates an exemplary embodiment of animage 1700 comprising a license plate image 1710. Although not shown,the license plate may be affixed to a vehicle which would also be partof the image. The image 1700 includes the license plate image 1710 and arectangle 1705. As shown in exemplary image portion 1700, an objectimage has been detected by the object detector 905 of the license platedetector 630. The object image in this example is the license plateimage 1710. After the object image was detected, the quad processor 910fit a rectangle 1705 around the license plate image 1710. Informationassociated with the rectangle may be provided to the rendering module650 for overlaying a rectangle around the detected license plate imagein the image displayed on the display 655.

FIG. 18 illustrates an exemplary embodiment of a portion of an image1800 comprising the same license plate image 1710 illustrated in FIG.17. Image portion 1800 includes the license plate image 1810 and aquadrilateral 1805. As discussed above with respect to FIGS. 10-12, thequad processor 910 of the license plate detector 630 performs severalfunctions to derive a quadrilateral that closely fits the detectedobject image. Once the quadrilateral has been derived, the quadprocessor 910 then computes the skew factor and/or keystone discussedabove.

Once the quadrilateral is determined to have a low skew (or a skew belowa threshold value) or the trapezoid has been determined to have a lowkeystone (or a keystone below a threshold value), the region(s) ofinterest detector 920 can dewarp the image to move one step closer toconfirming the presence of a license plate image in the image and toalso generate patch that is easily read by OCR software. In some aspectsof the apparatus, the patch is the license plate image that has beencropped out of the image.

FIG. 19 is an exemplary embodiment of the dewarping process beingperformed on a license plate image 1905 to arrive at license plate image1910. FIG. 19 illustrates two stages 1901 and 1902 of the dewarpingprocess.

As shown, the first stage 1901 illustrates the license plate image 1905in a trapezoidal shape similar to the shape of the quadrilateral 1805illustrated in FIG. 18. The second stage 1902 illustrates the licenseplate image 1910 after the dewarping process has been performed. Asshown, license plate image 1910 has undergone a perspective transformand rotation. The license plate image 1910 as shown in the second stage1902 is now in a readable rectangular shape. In some aspects of thedewarping process, the license plate image may also undergo correctionsif the license plate image is skewed or may scale the license plateimage to a suitable size.

The ability to accurately dewarp quadrilaterals and especially thequadrilaterals that are license plate images taken at any angle is anintegral piece of the license plate detection apparatus. The dewarpingcapability enables a user to capture an image of a license plate at avariety of different angles and distances. For instance, the image maybe taken with any mobile apparatus at virtually any height, direction,and/or distance. Additionally, it provides the added benefit of beingable to capture a moving image from any position. Once the license plateimage has been dewarped, the region(s) of interest detector 920 willcrop the rectangular license plate image to generate a patch. The patchwill be used for further confirmation that the license plate image 1910is, in fact, an image of a license plate.

FIG. 20 conceptually illustrates an exemplary embodiment of a process2000 for processing an image comprising a license plate image. Theprocess 2000 may be performed by a license plate detection apparatus.The process 2000 may start after the license plate detection apparatushas instantiated an application that enables the image capture featureof a mobile apparatus.

As shown, the process 2000 detects (at 2010) at least one object image,similar to the object image detection performed by process 1600. Thefollowing describes in greater detail the process of processing (at1620) the image.

For instance, the process 2000 then fits (at 2015) a rectangle to eachdetected object image in order to reduce the search space to thedetected object images. The information associated with the rectanglemay also be used as an overlay to indicate to users of the license platedetection apparatus the location(s) of the detected object image(s). Theprocess then uses the rectangles to form (at 2020) a convex hull aroundeach object image. The convex hull, as discussed above, is a polygon ofseveral vertices and edges that fits closely around an object imagewithout having any edges that overlap the object image.

At 2025, the process 2000 compresses the convex hull to a quadrilateralthat closely fits around the detected object image. The process ofcompressing the convex hull into a quadrilateral was discussed in detailwith respect to FIGS. 9-12. The process 2000 then filters (at 2030)duplicate rectangles and/or quadrilaterals. In some aspects of theprocess, rectangles or quadrilaterals that are similar in size andoverlap may be discarded based on some set criteria. For example, thesmaller rectangle and/or quadrilateral may be discarded.

The process 2000 calculates (at 2035) a skew factor. The process 2000then dewarps (at 2040) the quadrilateral. The process then crops (at2045) the object image within the quadrilateral, which becomes thepatch. The patch will be used for further processing as discussed below.In some aspects of the process, the object image is cropped at aparticular ratio that is common for license plates of a particularregion or type. For instance, the process may crop out a 2:1 aspectratio patch, of the image, which is likely to contain the license plateimage. Once the quadrilateral is cropped, the process 2000 then ends.

FIG. 21 illustrates an exemplary embodiment of a diagram that determineswhether a patch 2100 is an actual license plate image. The patch 2100includes a candidate license plate image 2105, alpha-numeric characters2120 and 2140, rectangles 2115, sloped lines 2130, zero-slope line 2110,and graphic 2125.

As shown in the patch 2100, rectangles are fit around detected objectimages within the patch. In some aspects of the apparatus, object imagesmay be detected using the MSER object detection method. Conjunctively orconversely, some aspects of the apparatus, may use edge and or cornerdetection methods to detect the object images. In this case, thedetected object images are alpha-numeric characters 2120 and 2140 aswell as graphic 2125. After detecting the alpha-numeric characters 2120and 2140 as well as graphic 2125, a stroke width transform (SWT) may beperformed to partition the detected object images into those that arelikely from an alpha-numeric character and those that are not. Forinstance, the SWT may try to capture the only alpha-numeric effectivefeatures and use certain geometric signatures of alpha-numericcharacters to filter out non-alpha-numeric areas, resulting in morereliable text regions. In such instances, the SWT transform maypartition the alphanumeric characters 2120 and 2140 from the graphic2125. Thus, only those object images that are determined to likely bealpha-numeric characters, such as alphanumeric characters 2120 and 2140,are later used in a scoring process to be discussed below. In someaspects of the apparatus, some object images other than alpha-numericcharacters may pass through the SWT partitioning. Thus, furtherprocessing may be necessary to filter out the object images that are notalpha-numeric characters and also to determine whether the alpha-numericcharacters in the license plate image fit the characteristics common fora license plate images.

Following the partitioning of alpha-numeric characters from non-alphanumeric characters, a line is fit to the center of the rectangle pairfor each pair of rectangles. For instance, a sloped line is shown forthe D and object image 2125 pair. The distance of all other rectanglesto the lines 2130 and 2110 are accumulated and the pair with thesmallest summed distance is used as a text baseline. For instance, thezero-slope line 2110 has the smallest summed distance of the rectanglesto the line 2110. Some aspects of the apparatus may implement a scoringprocess to determine the presence of a license plate image. Forinstance, some aspects of the scoring process may determine a score forthe determined alpha-numeric characters on the zero-slope line 2110. Thescore may increase when the rectangle around the alpha-numeric characteris not rotated beyond a threshold amount. The score may decrease if thedetected alpha-numeric character is too solid. In some aspects of thescoring process, solidity may be defined as the character area/rectanglearea. When the calculated area is over a threshold amount, then thedetected object image may be deemed too solid and the score decreases.

In other aspects of the scoring process, for each rectangle 2115 in thepatch 2100 the patch score increases by some scoring value if the centerof the rectangle is within a particular distance of the baseline linewhere X is the shorter of the rectangle height and width. For instance,if the particular distance were to be defined as the shorter of therectangle height and width and if the scoring value is set at 1, thepatch score value of the patch 2100 would be 7 because the rectanglesaround the characters “1DDQ976” are within a shorter distance than thewidth of the rectangle. Furthermore, the zero-slope of the line 2110between the alpha-numeric characters 2120 further confirm that thispatch is likely a license plate image since typically license plateshave a string of characters along a same line. Sloped lines 2130 are,therefore, unlikely to provide any indication that the patch is alicense plate image because the distance between characters is too greatand the slope is indicative of a low likelihood of a license plateimage. Accordingly, in some aspects of the process, sloped lines 2130are discarded in the process.

In some aspects of the process, when the patch has a score above athreshold value, the patch is determined to be a license plate image,and the license plate detection is complete. The license plate imagedata is then transmitted to a server for further processing and for usein other functions computed by the server, the results of which areprovided to the license plate detection apparatus.

FIG. 22 conceptually illustrates an exemplary embodiment of a process2200 for processing a patch comprising a candidate license plate imagesuch as patch 2100. The process may be performed by the license platedetection apparatus. The process may begin after a patch has beencropped from an image file.

As shown, the process 2200 processes (at 2205) only substantiallyrectangular portion(s) of the patch to locate alpha-numeric characters.The process 2200 fits (at 2210) rectangles around the locatedalpha-numeric characters and computes scores based on the distancesbetween rectangle pairs as discussed above with respect to FIG. 21. Theprocess 2200 selects (at 2215) the patch with the best score torecognize as a license plate image. Alternatively or conjunctively, theprocess 2200 may select all patches that have a score above a thresholdlevel to be deemed as license plate images. In such instances, multiplepatches, or instances of license plate information, would be transmittedto the server for further processing.

FIG. 23 illustrates an exemplary embodiment of vehicle financeunderwriting data flow 2300. The data flow includes license plate image2305, VIN 2310, vehicle configuration (e.g., VIN explosion) 2315,financing offer 2330, a driver's license image 2320, and driver'slicense information 2335.

As shown, the data flow begins with the license plate image 2305. TheVIN 2310 is the recovered from the license plate image 2305. Then thevehicle configuration 2315 is recovered from the VIN 2310. The vehicleconfiguration 2315 may include vehicle details such as features, trim,transmission type, last read odometer, and an estimated value. Suchconfiguration details may be acquired from several different servicesusing the VIN 2310 to retrieve the details.

Concurrently or consecutively the driver's license image 2320 may beprocessed by the apparatus. For instance, the apparatus may prompt auser to capture a driver's license image before or after capturing thelicense plate image. The driver's license information 2335 is thenrecovered from the driver's license image. Such information may berecovered by using OCR software similar to the OCR software used torecover the license plate information. The recovered driver's licenseinformation 2335 may include information such as state, number, name,address, date of birth and any other pertinent information that mayassist in providing the refinance offer as well as underwriting therequestor for the loan. The driver's license information 2335 and theVIN explosion 2315 is then used to generate the vehicle financing offer.As discussed above, the offer may be adjustable based on user input tochange the term and/or details of the loan. The above described dataflow is helpful in streamlining the vehicle financing process because auser may be underwritten for a vehicle loan in near real time with asimple image.

FIG. 24 illustrates an exemplary embodiment of an operating environment2400 for communication between a gateway 2495 and client apparatuses2410, 2430, and 2470. In some aspects of the service, client apparatuses2410, 2430, and 2470 communicate over one or more wired or wirelessnetworks 2440 and 2460. For example, wireless network 2440, such as acellular network, can communicate with a wide area network (WAN) 2480,such as the internet, by use of mobile network gateway 2450. A mobilenetwork gateway in some aspects of the service provides a packetoriented mobile data service or other mobile data service allowingwireless networks to transmit data to other networks, such as the WAN2480. Likewise, access device 2460 (e.g., IEEE 802.11b/g/n wirelessaccess device) provides communication access to the WAN 2480. Theapparatuses 2410, 2430, and 2470 can be any portable electroniccomputing apparatus capable of communicating with the gateway 2495. Forinstance, the apparatuses 2410 and 2470 may have an installedapplication that is configured to communicate with the gateway 2495. Theapparatus 2430 may communicate with the gateway 2495 through a websitehaving a particular URL. Alternatively, the apparatus 2430 may be anon-portable apparatus capable of accessing the internet through a webbrowser.

In order to process the license plate information to provide a financingoffer for a vehicle, the gateway 2495 may also communicate with thirdparty services 2490 that provide information such as vehicleconfiguration and vehicle identification numbers (VIN)s. Additionally,the gateway 2495 may also communicate with various loan underwritingservices. As shown, the gateway 2495 may communicate directly with atleast one third party processing service 2490 if such services arelocated on the same network as the gateway 2495. Alternatively, thegateway 2495 may communicate with at least one of the third partyprocessing services 2490 over the WAN 2480 (e.g., the internet).

In some aspects of the service, the process for providing a vehicle loanoffer may incorporate the location with the vehicle and/or device. Insuch aspects, the service may optionally use location informationacquired through a GPS satellite 2420. The apparatuses 2410 and 2470 maybe configured to use a GPS service and provide location information tothe gateway 2495 using the connections discussed above. The providedlocation information may be used by the vehicle 2495 to adjust therequisite criteria for obtaining a vehicle financing offer based ongeographic region provided GPS information. Thus, the service describedin FIG. 24 provides greater granularity and accuracy obtaining afinancing offer, which may then be used to underwrite a user for avehicle loan in near real time.

FIG. 25 illustrates a detailed exemplary flow of data between a gateway2500 and various other modules. The gateway 2500 may be similar to thegateway 645 described with respect to FIG. 6. The gateway 2500 andmodules 2510-2560 may be located on a server such as the server 230. Insome aspects of the apparatus, the gateway 2500 may be a request routerin that it receives requests from the various modules 2510-2560 androutes the requests to at least one of the appropriate module 2510-2560.The gateway 2500 communicates with various modules 2510-2560, which maycommunicate with various third party services to retrieve data thatenables the gateway 2500 to provide a financing offer to a clientapparatus 2570 from an image of a license plate.

As shown, the client apparatus 2570, may use a network interface 2560 totransmit at least one license plate image and a driver's license imagerecovered from optical images taken by the client apparatus 2570. Theclient apparatus 2570 may include an installed application providinginstructions for how to communicate with the gateway 2500 through thenetwork interface 2560. In this example, the network interface 2560provides license plate image information or text input of a licenseplate to the gateway 2500, as well as, driver's license information tothe gateway 2500. For instance, as discussed above, the networkinterface 2560 may transmit text strings received as user input at theclient apparatus 2570 or a license plate image processed by the clientapparatus 2570 to the gateway 2500. As further shown in this example,the gateway 2500 may route the license plate image data and the driver'slicense image data to the OCR module 2510 to perform the OCR textextraction of the license plate information and driver's licenseinformation. In this example, the OCR module 2510 may have specializedor a commercial OCR software application installed that enables accurateextraction of the license plate number and state. The OCR module may besimilar to the OCR module discussed in FIG. 6. In one example, the OCRmodule 2510 may also have the capability of determining if a licenseplate image contains a clear enough image that will provide for accuratetext extraction. In this example, if the license plate image does notcontain a clear image or the image quality is too low, the OCR modulemay alert the gateway 2500 to transmit a warning to the client apparatus2570. In an alternative example, a license plate image may be recoveredand transmitted to the gateway 2500 for further processing.

Once the license plate number and state information is extracted andconverted to text strings, the gateway 2500 will provide the extractedtext to a translator 2520, which is capable of determining a VIN fromthe license plate information. The translator 2520 may communicate withthird party services using functionality provided in an applicationprogramming interface (API) associated with the third party services.Such services may derive VINs from license plate information to retrievethe vehicle's VIN. In some aspects of the server, the various modules2510-2550 may also be configured to communicate with the third partyservices or apparatuses (not shown) using APIs associated with the thirdparty services. In such aspects of the server, each module 2510-2550 mayroute a request through the gateway 2500 to the network interface 2560,which will communicate with the appropriate third party service (notshown).

The gateway 2500 then routes the retrieved VIN to the VIN decoder 2530along with a request to generate a VIN explosion. The VIN decoder 2530is capable of using the VIN to generate a VIN explosion by requestingthe VIN explosion from a third party service. The VIN explosion includesall of the features, attributes, options, and configurations of thevehicle associated with the VIN (and the license plate image). In someaspects of the apparatus, the VIN explosion may be provided as an arrayof data, which the gateway 2500 or VIN decoder 2530 is capable ofunderstanding, processing, and/or routing accurately. Similar to the VINtranslator 2520, the VIN decoder 2530 may communicate with a third partyservice by using an API associated with the service. Additionally, theVIN may be provided to a module capable of recovering a vehicle historyreport from the VIN (not shown). Such a module may then receiveinformation regarding the last odometer reading, which may also betransmitted to the gateway 2500 for further processing. Additionally,the VIN and/or vehicle data derived from the VIN explosion may be routedback through the gateway 2500 and through the network interface 2560 tothe client apparatus 2570.

The vehicle data from the VIN explosion may be routed from the gatewayto the valuation module 2540 with a request to derive an estimated valuefor a vehicle. The valuation module 2540, of some aspects, may queryseveral different value estimation services for vehicles such as KelleyBlue Book, Black Book, and other vehicle sales listing services or valueestimation services to obtain an estimated value for a vehicle. Thevaluation module 2540 may also aggregate all of the data obtained fromthe various estimation and listing services to produce a more reliableand accurate vehicle value estimate. Additionally, the valuation module2540 may receive geographic information (not shown) about the locationof the vehicle, which the gateway 2500 may obtain over the networkinterface 2560. Such information may be used to generate a highlyaccurate estimation of the value for the vehicle, which a loanunderwriting service may use to assess the risk associated withfinancing the particular vehicle.

The vehicle financing offer generator 2550 may use the driver's licenseinformation, VIN explosion, and estimated value of the vehicle tounderwrite a user for a loan. Once underwritten, at least one accuratefinancing offer may be transmitted through the gateway 2500 to theapparatus 2570.

Once generated, the financing offer may then be routed through thegateway 2500 to the network interface 2560 for display on the apparatus2570. In some aspects of the apparatus, a user may wish to obtain avehicle loan based on the received offer and other adjusted informationdescribed with respect to FIG. 3. In such aspects, an interaction withthe apparatus 2570 may initiate an action by the vehicle financing offergenerator 2550 to fund an actual loan based on many of the factorsdiscussed above. However, one of ordinary skill in the art willrecognize that several different factors may go into funding a loan fora vehicle. Such factors may either be retrieved from third party serviceor provided by user interaction with the apparatus 2570.

Providing the option to preview and adjust the financing offer, as wellas, being underwritten for a vehicle loan based on capturing a couple ofimages, gives the user a simple and efficient way to finance a vehicle.For instance, once a user is satisfied with the loan offer and details,the user may be underwritten for a loan which may fund in near realtime. Thus, the process of purchasing a vehicle becomes simplifiedbecause any mobile apparatus user may be underwritten for a loan basedon the requisite images.

FIG. 26 conceptually illustrates an exemplary embodiment of a process2600 for transmitting a vehicle finance offer from a license plateimage. The process 2600 may be performed by a server such as the server230. The process may begin after a mobile apparatus has recovered asuitable license plate image for transmission to the server 230 and/or adriver's license image.

As shown, the process 2600 receives (at 2610) license plate imageinformation or text input from a mobile apparatus. The text input may beinformation associated with a vehicle license plate such as a state andalpha-numeric characters. The process 2600 then requests (at 2630) a VINassociated with the license plate information. The process 2600 mayrequest the VIN by sending the request to a third party server. In someaspects of the server, the process 2600 communicates with the thirdparty server by using an API.

At 2640, the process 2600 receives the VIN associated with the licenseplate information. The process 2600 then requests (at 2650) a vehicleconfiguration and history using the received VIN. The vehicleconfiguration may include different features and options that areequipped on the vehicle. For instance, such features and options mayinclude different wheel sizes, interior trim, vehicle type (e.g., coupe,sedan, sport utility vehicle, convertible, truck, etc.), sound system,suspension, and any other suitable vehicle configuration or featuretypically found in a vehicle. The history may include the last knownodometer reading. The vehicle configuration may be the VIN explosiondiscussed in the previous figure. The process 2600 receives (at 2660)the vehicle configuration data and history.

At 2680, the process 2600 receives information from a driver's licenseimage from the mobile apparatus. Such information may be useful fordetermining the requestor's creditworthiness so that an accurate vehiclefinancing offer may be generated. The process 2600 requests (at 2685)vehicle valuation based on the vehicle configuration data. The valuationinformation may be used to assess the risk associated with the requestedloan and adjust the vehicle financing offer accordingly. At 2687, theprocess receives the vehicle valuation data based on the vehicleconfiguration data.

At 2690, the process 2600 requests a vehicle financing offer from atleast one entity capable of loan underwriting. Such a quote may be anestimate based on the vehicle configuration, user's creditworthiness,and value of the vehicle. All of these factors may be used by variousloan underwriters to assess the risk involved with making the loan offerand generate the offer accordingly. The process 2600 transmits (at 2695)the vehicle financing offer to the mobile apparatus for display at theapparatus. Then the process ends.

FIG. 27 illustrates an exemplary embodiment of a system 2700 that mayimplement the license plate detection apparatus. The electronic system2700 of some embodiments may be a mobile apparatus. The electronicsystem includes various types of machine readable media and interfaces.The electronic system includes a bus 2705, processor(s) 2710, read onlymemory (ROM) 2715, input device(s) 2720, random access memory (RAM)2725, output device(s) 2730, a network component 2735, and a permanentstorage device 2740.

The bus 2705 communicatively connects the internal devices and/orcomponents of the electronic system. For instance, the bus 2705communicatively connects the processor(s) 2710 with the ROM 2715, theRAM 2725, and the permanent storage 2740. The processor(s) 2710 retrieveinstructions from the memory units to execute processes of theinvention.

The processor(s) 2710 may be implemented with one or moregeneral-purpose and/or special-purpose processors. Examples includemicroprocessors, microcontrollers, DSP processors, and other circuitrythat can execute software. Alternatively, or in addition to the one ormore general-purpose and/or special-purpose processors, the processormay be implemented with dedicated hardware such as, by way of example,one or more FPGAs (Field Programmable Gate Array), PLDs (ProgrammableLogic Device), controllers, state machines, gated logic, discretehardware components, or any other suitable circuitry, or any combinationof circuits.

Many of the above-described features and applications are implemented assoftware processes of a computer programming product. The processes arespecified as a set of instructions recorded on a machine readablestorage medium (also referred to as machine readable medium). When theseinstructions are executed by one or more of the processor(s) 2710, theycause the processor(s) 2710 to perform the actions indicated in theinstructions.

Furthermore, software shall be construed broadly to mean instructions,data, or any combination thereof, whether referred to as software,firmware, middleware, microcode, hardware description language, orotherwise. The software may be stored or transmitted over as one or moreinstructions or code on a machine-readable medium. Machine-readablemedia include both computer storage media and communication mediaincluding any medium that facilitates transfer of a computer programfrom one place to another. A storage medium may be any available mediumthat can be accessed by the processor(s) 2710. By way of example, andnot limitation, such machine-readable media can comprise RAM, ROM,EEPROM, CD-ROM or other optical disk storage, magnetic disk storage orother magnetic storage devices, or any other medium that can be used tocarry or store desired program code in the form of instructions or datastructures and that can be accessed by a processor. Also, any connectionis properly termed a machine-readable medium. For example, if thesoftware is transmitted from a website, server, or other remote sourceusing a coaxial cable, fiber optic cable, twisted pair, digitalsubscriber line (DSL), or wireless technologies such as infrared (IR),radio, and microwave, then the coaxial cable, fiber optic cable, twistedpair, DSL, or wireless technologies such as infrared, radio, andmicrowave are included in the definition of medium. Disk and disc, asused herein, include compact disc (CD), laser disc, optical disc,digital versatile disc (DVD), floppy disk, and Blu-ray® disc where disksusually reproduce data magnetically, while discs reproduce dataoptically with lasers. Thus, in some aspects machine-readable media maycomprise non-transitory machine-readable media (e.g., tangible media).In addition, for other aspects machine-readable media may comprisetransitory machine-readable media (e.g., a signal). Combinations of theabove should also be included within the scope of machine-readablemedia.

Also, in some embodiments, multiple software inventions can beimplemented as sub-parts of a larger program while remaining distinctsoftware inventions. In some embodiments, multiple software inventionscan also be implemented as separate programs. Any combination ofseparate programs that together implement a software invention describedhere is within the scope of the invention. In some embodiments, thesoftware programs, when installed to operate on one or more electronicsystems 2700, define one or more specific machine implementations thatexecute and perform the operations of the software programs.

The ROM 2715 stores static instructions needed by the processor(s) 2710and other components of the electronic system. The ROM may store theinstructions necessary for the processor(s) 2710 to execute theprocesses provided by the license plate detection apparatus. Thepermanent storage 2740 is a non-volatile memory that stores instructionsand data when the electronic system 2700 is on or off. The permanentstorage 2740 is a read/write memory device, such as a hard disk or aflash drive. Storage media may be any available media that can beaccessed by a computer. By way of example, the ROM could also be EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium that can be used to carryor store desired program code in the form of instructions or datastructures and that can be accessed by a computer.

The RAM 2725 is a volatile read/write memory. The RAM 2725 storesinstructions needed by the processor(s) 2710 at runtime, the RAM 2725may also store the real-time video images acquired during the licenseplate detection process. The bus 2705 also connects input and outputdevices 2720 and 2730. The input devices enable the user to communicateinformation and select commands to the electronic system. The inputdevices 2720 may be a keypad, image capture apparatus, or a touch screendisplay capable of receiving touch interactions. The output device(s)2730 display images generated by the electronic system. The outputdevices may include printers or display devices such as monitors.

The bus 2705 also couples the electronic system to a network 2735. Theelectronic system may be part of a local area network (LAN), a wide areanetwork (WAN), the Internet, or an Intranet by using a networkinterface. The electronic system may also be a mobile apparatus that isconnected to a mobile data network supplied by a wireless carrier. Suchnetworks may include 3G, HSPA, EVDO, and/or LTE.

It is understood that the specific order or hierarchy of steps in theprocesses disclosed is an illustration of exemplary approaches. Basedupon design preferences, it is understood that the specific order orhierarchy of steps in the processes may be rearranged. Further, somesteps may be combined or omitted. The accompanying method claims presentelements of the various steps in a sample order, and are not meant to belimited to the specific order or hierarchy presented.

The various aspects of this disclosure are provided to enable one ofordinary skill in the art to practice the present invention. Variousmodifications to exemplary embodiments presented throughout thisdisclosure will be readily apparent to those skilled in the art, and theconcepts disclosed herein may be extended to other apparatuses, devices,or processes. Thus, the claims are not intended to be limited to thevarious aspects of this disclosure, but are to be accorded the fullscope consistent with the language of the claims. All structural andfunctional equivalents to the various components of the exemplaryembodiments described throughout this disclosure that are known or latercome to be known to those of ordinary skill in the art are expresslyincorporated herein by reference and are intended to be encompassed bythe claims. Moreover, nothing disclosed herein is intended to bededicated to the public regardless of whether such disclosure isexplicitly recited in the claims. No claim element is to be construedunder the provisions of 35 U.S.C. § 112(f) unless the element isexpressly recited using the phrase “means for” or, in the case of amethod claim, the element is recited using the phrase “step for.”

What is claimed is:
 1. A mobile apparatus, comprising: an image sensorconfigured to capture an optical image including an image of a vehiclelicense plate; a license plate detector configured to: identify andcrop, by a processor, one or more object images from the capturedoptical image with each of said one or more cropped object imagescomprising a candidate vehicle license plate image having a plurality ofalphanumeric characters, score, by the processor, each of said croppedone or more of the object images based on a probability that therespective cropped object image comprises the vehicle license plateimage, wherein each of the cropped one or more object images is scoredby: providing a rectangular fitting around each character of theplurality of alphanumeric characters in the one or more object images,fitting a line to a center of the respective rectangular fittings ofeach pair of the plurality of alphanumeric characters, for each fittedline, summing a distance of all other respective rectangular fittings tothe respective fitted line, selecting the respective pair ofalphanumeric characters corresponding to the respective fitted line witha smallest summed distance of all of the fitted lines as a text baselinefor the candidate vehicle license plate image, computing a score for thecandidate vehicle license plate image having the selected text baselinebased at least on the rectangular fitting around each of thealphanumeric characters, relative positions of each of the alphanumericcharacters relative to each other within the respective cropped objectimage and a calculated slope of the fitted line for the selected pair ofalphanumeric characters, and recover information relating to the vehiclelicense plate in response to the calculated score of the candidatevehicle license plate imageconfirming that the respective cropped objectimage corresponds to the vehicle license plate; and an interfaceconfigured to transmit the recovered information relating to the vehiclelicense plate to a remote apparatus and receive a vehicle financingoffer in response to the transmission.
 2. The mobile apparatus of claim1, wherein the license plate detector is further configured to recoverthe information from the selected one of said one or more of the croppedobject images.
 3. The mobile apparatus of claim 1, wherein the licenseplate detector is further configured to process the captured opticalimage to dewarp at least one of said one or more of the object images.4. The mobile apparatus of claim 1, further comprising a display and arendering module configured to render the optical image to the displayand overlay a detection indicator on each of said one or more of thecropped object images in the optical image.
 5. The mobile apparatus ofclaim 1, further comprising an image filter configured to: apply a setof filter parameters to the captured optical image; and dynamicallyadjust the parameters based on at least one of color temperature,ambient light, object image location, and the location of the apparatus.6. The mobile apparatus of claim 1, wherein the license plate detectoris further configured to apply a straight line through each of the pairsof alphanumeric characters within each respective cropped object image,such that at least one pair of alphanumeric characters is discarded as acandidate vehicle license plate image if the straight line through therespective pair of alphanumeric characters has a non-zero slope relativeto a horizontal direction of the candidate license plate.
 7. A mobileapparatus, comprising: an image sensor configured to capture an opticalimage, wherein the captured optical image comprises a plurality ofobject images, and wherein one of the plurality of object imagescomprises an image of a vehicle license plate; a license plate detectorconfigured to: identify and crop, by a processor, one or more objectimages from the captured optical image with each of said one or morecropped object images comprising a candidate vehicle license plate imagehaving a plurality of alphanumeric characters, score, by the processor,each of said cropped one or more of the object images based on aprobability that the respective cropped object image comprises thevehicle license plate image, wherein each of the cropped one or moreobject images is scored by: providing a rectangular fitting around eachcharacter of the plurality of alphanumeric characters in the one or moreobject images, fitting a line to a center of the respective rectangularfittings of each pair of the plurality of alphanumeric characters, foreach fitted line, summing a distance of all other respective rectangularfittings to the respective fitted line, selecting the respective pair ofalphanumeric characters corresponding to the respective fitted line witha smallest summed distance of all of the fitted lines as a text baselinefor the candidate vehicle license plate image, computing a score for thecandidate vehicle license plate image having the selected text baselinebased at least on the rectangular fitting around each of thealphanumeric characters, relative positions of each of the alphanumericcharacters relative to each other within the respective cropped objectimage and a calculated slope of the fitted line for the selected pair ofalphanumeric characters, and recover information relating to the vehiclelicense plate in response to the calculated score of the candidatevehicle license plate image confirming that the respective croppedobject image corresponds to the vehicle license plate; and an interfaceconfigured to transmit the recovered information relating to the vehiclelicense plate to a remote apparatus and receive a vehicle financingoffer in response to the transmission.
 8. The mobile apparatus of claim7, wherein the license plate detector is further configured to processthe captured optical image to dewarp said one of the object images priorto recovering the information.
 9. The mobile apparatus of claim 7,further comprising a display and a rendering module configured to renderthe optical image to the display and overlay a detection indicator oneach of the plurality of object images.
 10. The mobile apparatus ofclaim 9, wherein the rendering module is further configured to overlay afirst detection indicator over said one of the plurality of objectimages and a second detection indicator over the other object images ofsaid plurality of object images.
 11. The mobile apparatus of claim 10,wherein the rendering module is further configured to cause the firstdetection indicator to appear visually different from the seconddetection indicator on the display.
 12. The mobile apparatus of claim 7,wherein the license plate detector is further configured to process thecaptured optical image to score each of the plurality of object imagesbased on a probability that the each of the plurality of object imagescomprises the vehicle license plate image.
 13. The mobile apparatus ofclaim 12, wherein the license plate detector is further configured toselect said one of the plurality of object images based on the selectedobject image's score.
 14. A mobile apparatus, comprising: an imagesensor configured to capture an optical image comprising an image of avehicle license plate; a display; a rendering module configured torender the captured optical image to the display; an image filterconfigured to apply one or more filter parameters to the capturedoptical image based on at least one of color temperature of the image,ambient light, and motion of the apparatus; a license plate detectorconfigured to: identify and crop, by a processor, one or more objectimages from the captured optical image with each of said one or morecropped object images comprising a candidate vehicle license plate imagehaving a plurality of alphanumeric characters, score, by the processor,each of said cropped one or more object images based on a probabilitythat the respective cropped object image comprises the vehicle licenseplate image, wherein each of the cropped one or more object images isscored by: providing a rectangular fitting around each character of theplurality of alphanumeric characters in the one or more object images,fitting a line to a center of the respective rectangular fittings ofeach pair of the plurality of alphanumeric characters, for each fittedline, summing a distance of all other respective rectangular fittings tothe respective fitted line, selecting the respective pair ofalphanumeric characters corresponding to the respective fitted line witha smallest summed distance of all of the fitted lines as a text baselinefor the candidate vehicle license plate image, computing a score for thecandidate vehicle license plate image having the selected text baselinebased at least on the rectangular fitting around each of thealphanumeric characters, relative positions of each of the alphanumericcharacters relative to each other within the respective cropped objectimage and a calculated slope of the fitted line for the selected pair ofalphanumeric characters, and recover information relating to the vehiclelicense plate in response to the calculated score of the candidatevehicle license plate image confirming that the respective croppedobject image corresponds to the vehicle license plate wherein therendering module is further configured to overlay a detection indicatoron the displayed optical image to assist a user in positioning theapparatus with respect to the optical image in response to a signal fromthe image filter; and wherein the rendering module is further configuredto provide an alert to the display when the license plate detector failsto recover the vehicle license plate information; and an interfaceconfigured to transmit the recovered information relating to the vehiclelicense plate to a remote apparatus and receive a vehicle financingoffer in response to the transmission.
 15. The mobile apparatus of claim14, wherein the license plate detector is further configured to processthe captured optical image to dewarp at least one of said one or more ofthe plurality of object images.
 16. A computer program product for amobile apparatus having an image sensor configured to capture an opticalimage, the captured optical image including a plurality of objectimages, the computer program product comprising: a non-transitorymachine readable medium comprising code to: select and crop, by aprocessor, said one of the object images from the captured optical imagewith each of said one or more cropped object images comprising acandidate vehicle license plate image having a plurality of alphanumericcharacters; score, by the processor, each of said cropped one or moreobject images based on a probability that the respective cropped objectimage comprises a vehicle license plate, wherein each of the cropped oneor more object images is scored by: providing a rectangular fittingaround each character of the plurality of alphanumeric characters in theone or more object images, fitting a line to a center of the respectiverectangular fittings of each pair of the plurality of alphanumericcharacters, for each fitted line, summing a distance of all otherrespective rectangular fittings to the respective fitted line, selectingthe respective pair of alphanumeric characters corresponding to therespective fitted line with a smallest summed distance of all of thefitted lines as a text baseline for the candidate vehicle license plateimage, computing a score for the candidate vehicle license plate imagehaving the selected text baseline based at least on the rectangularfitting around each of the alphanumeric characters, relative positionsof each of the alphanumeric characters relative to each other within therespective cropped object image and a calculated slope of the fittedline for the selected pair of alphanumeric characters, and recoverinformation relating to the vehicle license plate in response to thecalculated score of the candidate vehicle license plate image confirmingthat the respective cropped object image corresponds to the vehiclelicense plate transmit the vehicle license plate information to a remoteapparatus; and receive a vehicle financing offer in response to thetransmission.
 17. The computer program product of claim 16, wherein thecode to process the captured optical image is configured to process thecaptured optical image to identify one or more of the object images fromthe captured optical image, each of said one or more of the objectimages comprising a candidate vehicle license plate image.
 18. Thecomputer program product of claim 17, wherein the code for processingthe captured optical image is further configured to dewarp at least oneof said one or more of the object images.
 19. The computer programproduct of claim 16, wherein the code to process the captured opticalimage is further configured to select said one of the object images fromsaid one or more of the cropped object images based on the scores. 20.The computer program product of claim 19, wherein the code to processthe captured optical image is further configured to recover theinformation from the selected one of said identified one or more of thecropped object images.
 21. A mobile apparatus, comprising: an imagesensor configured to capture an optical image including an image of avehicle license plate; a timing circuit configured to sample anelectrical signal corresponding to the captured optical image at a framerate; a license plate detector configured to; identify and crop, by aprocessor, one or more object images from the captured optical imagewith the one or more cropped object images having a plurality ofalphanumeric characters, score, by the processor, each of said croppedone or more object images based on a probability that the respectivecropped object image comprises the vehicle license plate image, whereineach of the cropped one or more object images is scored by: providing arectangular fitting around each character of the plurality ofalphanumeric characters in the one or more object images, fitting a lineto a center of the respective rectangular fittings of each pair of theplurality of alphanumeric characters, for each fitted line, summing adistance of all other respective rectangular fittings to the respectivefitted line, selecting the respective pair of alphanumeric characterscorresponding to the respective fitted line with a smallest summeddistance of all of the fitted lines as a text baseline for the croppedone or more images, computing a score for the cropped one or more imageshaving the selected text baseline based at least on the rectangularfitting around each of the alphanumeric characters, relative positionsof each of the alphanumeric characters relative to each other within therespective cropped object image and a calculated slope of the fittedline for the selected pair of alphanumeric characters, and recoverinformation relating to the vehicle license plate image in response tothe calculated score confirming that the respective cropped object imagecorresponds to the vehicle license plate; and an interface configured totransmit the recovered information relating to the vehicle license plateto a remote apparatus and receive a vehicle financing offer in responseto the transmission.
 22. The apparatus of claim 21, wherein the sampledelectrical signal comprises a plurality of samples, and wherein thelicense plate detector is further configured to identify one or moreobject images from at least one of the plurality of samples.
 23. Theapparatus of claim 22, wherein at least one of said one or more of theobject images comprising a candidate vehicle license plate image. 24.The apparatus of claim 23, wherein the license plate detector is furtherconfigured to process said at least one of the plurality of samples toscore each of said one or more of the object images based on aprobability that the object image comprises the vehicle license plateimage.
 25. The apparatus of claim 24, wherein the license plate detectoris further configured to process at least first and second samples ofthe plurality of samples to score each of said one or more of the objectimages from each of said at least first and second samples.
 26. Theapparatus of claim 25, wherein each of said one or more object imagesdetected in the first sample is associated with location information,and wherein the license plate detector is further configured to use thelocation information to estimate the location of said one or more of theobjects in the second sample.
 27. The apparatus of claim 24, wherein thelicense plate detector is further configured to stop processing thesampled electrical signal when said score is above a threshold value.28. The apparatus of claim 22, further comprising a display, whereineach sample represents a frame of a video image presented to thedisplay; and a rendering module configured to render each frame of thevideo image to the display and overlay a detection indicator on each ofsaid one or more of the cropped object images.
 29. The apparatus ofclaim 28, wherein the overlay provides a visual indication of the objectimage associated with the score that is above a threshold value.